Molecular biology aims to understand cellular responses and regulatory dynamics in complex biological systems. However, these studies remain challenging in non-model species due to poor functional annotation of regulatory proteins. To overcome this limitation, we develop a multi-layer neural network that determines protein functionality directly from the protein sequence. We annotate kinases and phosphatases in Glycine max. We use the functional annotations from our neural network, Bayesian inference principles, and high resolution phosphoproteomics to infer phosphorylation signaling cascades in soybean exposed to cold, and identify Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as key temperature regulators. Importantly, the signaling cascade inference does not rely upon known kinase motifs or interaction data, enabling de novo identification of kinase-substrate interactions. Conclusively, our neural network shows generalization and scalability, as such we extend our predictions to Oryza sativa, Zea mays, Sorghum bicolor, and Triticum aestivum. Taken together, we develop a signaling inference approach for non-model species leveraging our predicted kinases and phosphatases.
Molecular biology aims to understand the molecular basis of cellular responses, unravel dynamic regulatory networks, and model complex biological systems. However, these studies remain challenging in non-model species as a result of poor functional annotation of regulatory proteins, like kinases or phosphatases. To overcome this limitation, we developed a multi-layer neural network that annotates proteins by determining functionality directly from the protein sequence. We annotated the kinases and phosphatases in the non-model species, Glycine max (soybean), achieving a prediction sensitivity of up to 97%. To demonstrate the applicability, we used our functional annotations in combination with Bayesian network principles to predict signaling cascades using time series phosphoproteomics. We shed light on phosphorylation cascades in soybean seedlings upon cold treatment and identified Glyma.10G173000 (TOI5) and Glyma.19G007300 (TOT3) as predicted key temperature response regulators in soybean. Importantly, the network inference does not rely upon known upstream kinases, kinase motifs, or protein interaction data, enabling de novo identification of kinase-substrate interactions. In addition to high accuracy and strong generalization, we showed that our functional prediction neural network is scalable to other model and non-model species, including Oryza sativa (rice), Zea mays(maize), Sorghum bicolor (sorghum), and Triticum aestivum (wheat). Taking together, we demonstrated a data-driven systems biology approach for non-model species leveraging our predicted upstream kinases and phosphatases.
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